BigMart Sales Predictions by Wesley Giles¶

Import necessary modules and data¶

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.offline as py
py.init_notebook_mode(connected=False)
import plotly.express as px


df = pd.read_csv("https://docs.google.com/spreadsheets/d/17_svn8lKuMPh4sl01a8Fca656yLRwbkYD2osTgmrvi8/export?format=csv")
df.head()
Out[1]:
Item_Identifier Item_Weight Item_Fat_Content Item_Visibility Item_Type Item_MRP Outlet_Identifier Outlet_Establishment_Year Outlet_Size Outlet_Location_Type Outlet_Type Item_Outlet_Sales
0 FDA15 9.30 Low Fat 0.016047 Dairy 249.8092 OUT049 1999 Medium Tier 1 Supermarket Type1 3735.1380
1 DRC01 5.92 Regular 0.019278 Soft Drinks 48.2692 OUT018 2009 Medium Tier 3 Supermarket Type2 443.4228
2 FDN15 17.50 Low Fat 0.016760 Meat 141.6180 OUT049 1999 Medium Tier 1 Supermarket Type1 2097.2700
3 FDX07 19.20 Regular 0.000000 Fruits and Vegetables 182.0950 OUT010 1998 NaN Tier 3 Grocery Store 732.3800
4 NCD19 8.93 Low Fat 0.000000 Household 53.8614 OUT013 1987 High Tier 3 Supermarket Type1 994.7052
In [2]:
!wget https://github.com/plotly/orca/releases/download/v1.2.1/orca-1.2.1-x86_64.AppImage -O /usr/local/bin/orca

!chmod +x /usr/local/bin/orca

!apt-get install xvfb libgtk2.0-0 libgconf-2-4

## Thanks to Greg Hogg https://youtu.be/qNF1HqBvpGE for this solution to Plotly export issues

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Reading package lists... Done
Building dependency tree       
Reading state information... Done
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libgconf-2-4 is already the newest version (3.2.6-4ubuntu1).
xvfb is already the newest version (2:1.19.6-1ubuntu4.10).
The following package was automatically installed and is no longer required:
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Use 'apt autoremove' to remove it.
0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.

Data Cleaning¶

Understand the shape of the data¶

In [3]:
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8523 entries, 0 to 8522
Data columns (total 12 columns):
 #   Column                     Non-Null Count  Dtype  
---  ------                     --------------  -----  
 0   Item_Identifier            8523 non-null   object 
 1   Item_Weight                7060 non-null   float64
 2   Item_Fat_Content           8523 non-null   object 
 3   Item_Visibility            8523 non-null   float64
 4   Item_Type                  8523 non-null   object 
 5   Item_MRP                   8523 non-null   float64
 6   Outlet_Identifier          8523 non-null   object 
 7   Outlet_Establishment_Year  8523 non-null   int64  
 8   Outlet_Size                6113 non-null   object 
 9   Outlet_Location_Type       8523 non-null   object 
 10  Outlet_Type                8523 non-null   object 
 11  Item_Outlet_Sales          8523 non-null   float64
dtypes: float64(4), int64(1), object(7)
memory usage: 799.2+ KB

Deal with duplicated data (if any)¶

In [4]:
df.duplicated().sum()
Out[4]:
0

Look for missing data¶

In [5]:
df.isna().sum()
Out[5]:
Item_Identifier                 0
Item_Weight                  1463
Item_Fat_Content                0
Item_Visibility                 0
Item_Type                       0
Item_MRP                        0
Outlet_Identifier               0
Outlet_Establishment_Year       0
Outlet_Size                  2410
Outlet_Location_Type            0
Outlet_Type                     0
Item_Outlet_Sales               0
dtype: int64

Item weight seems to be miissing quite a few values, so lets try to intercate the values¶

Here we based the intercated values on the mean of the rest if the items that share a type with¶
In [6]:
avg_weights_by_type = df.groupby("Item_Type")["Item_Weight"].mean()
for item_type in df["Item_Type"].unique():
  df.loc[df["Item_Type"] == item_type,["Item_Weight"]] = \
  df.loc[df["Item_Type"] == item_type,["Item_Weight"]].fillna(avg_weights_by_type[item_type])
df.isna().sum()
Out[6]:
Item_Identifier                 0
Item_Weight                     0
Item_Fat_Content                0
Item_Visibility                 0
Item_Type                       0
Item_MRP                        0
Outlet_Identifier               0
Outlet_Establishment_Year       0
Outlet_Size                  2410
Outlet_Location_Type            0
Outlet_Type                     0
Item_Outlet_Sales               0
dtype: int64

Lets see what values Outlet_Size has¶

In [7]:
df["Outlet_Size"].unique()
Out[7]:
array(['Medium', nan, 'High', 'Small'], dtype=object)

Attempting to crossmatch Outlet_Identifier to fill in Outlet Size¶

In [8]:
df.groupby("Outlet_Identifier")[["Outlet_Size","Outlet_Location_Type"]].count()
Out[8]:
Outlet_Size Outlet_Location_Type
Outlet_Identifier
OUT010 0 555
OUT013 932 932
OUT017 0 926
OUT018 928 928
OUT019 528 528
OUT027 935 935
OUT035 930 930
OUT045 0 929
OUT046 930 930
OUT049 930 930

No such luck so we will put "Unknown" as the size, as the remaining data may hold some insight¶

In [9]:
df["Outlet_Size"].fillna("Unknown", inplace=True)
df.isna().sum()
Out[9]:
Item_Identifier              0
Item_Weight                  0
Item_Fat_Content             0
Item_Visibility              0
Item_Type                    0
Item_MRP                     0
Outlet_Identifier            0
Outlet_Establishment_Year    0
Outlet_Size                  0
Outlet_Location_Type         0
Outlet_Type                  0
Item_Outlet_Sales            0
dtype: int64

"High also doesn't quite fit the them so we will change it to "Large"¶

In [10]:
df.loc[df["Outlet_Size"]=="High",["Outlet_Size"]] ="Large"
df["Outlet_Size"].unique()
Out[10]:
array(['Medium', 'Unknown', 'Large', 'Small'], dtype=object)

Look for inconsistant data¶

In [11]:
df.nunique()
Out[11]:
Item_Identifier              1559
Item_Weight                   431
Item_Fat_Content                5
Item_Visibility              7880
Item_Type                      16
Item_MRP                     5938
Outlet_Identifier              10
Outlet_Establishment_Year       9
Outlet_Size                     4
Outlet_Location_Type            3
Outlet_Type                     4
Item_Outlet_Sales            3493
dtype: int64

Item Fat Content looks a bit high so lets take a closer look¶

In [12]:
df["Item_Fat_Content"].unique()
Out[12]:
array(['Low Fat', 'Regular', 'low fat', 'LF', 'reg'], dtype=object)

Normalizing Item Fat Content¶

In [13]:
df["Item_Fat_Content"].replace(["low fat","LF"],"Low Fat", inplace=True)
df["Item_Fat_Content"].replace("reg","Regular", inplace=True)
df.nunique()
Out[13]:
Item_Identifier              1559
Item_Weight                   431
Item_Fat_Content                2
Item_Visibility              7880
Item_Type                      16
Item_MRP                     5938
Outlet_Identifier              10
Outlet_Establishment_Year       9
Outlet_Size                     4
Outlet_Location_Type            3
Outlet_Type                     4
Item_Outlet_Sales            3493
dtype: int64

Checking Item Type just to be sure¶

In [14]:
df["Item_Type"].unique()
Out[14]:
array(['Dairy', 'Soft Drinks', 'Meat', 'Fruits and Vegetables',
       'Household', 'Baking Goods', 'Snack Foods', 'Frozen Foods',
       'Breakfast', 'Health and Hygiene', 'Hard Drinks', 'Canned',
       'Breads', 'Starchy Foods', 'Others', 'Seafood'], dtype=object)

Finally checking numerical data for outliers¶

In [15]:
df.describe()
Out[15]:
Item_Weight Item_Visibility Item_MRP Outlet_Establishment_Year Item_Outlet_Sales
count 8523.000000 8523.000000 8523.000000 8523.000000 8523.000000
mean 12.857890 0.066132 140.992782 1997.831867 2181.288914
std 4.232804 0.051598 62.275067 8.371760 1706.499616
min 4.555000 0.000000 31.290000 1985.000000 33.290000
25% 9.310000 0.026989 93.826500 1987.000000 834.247400
50% 12.867061 0.053931 143.012800 1999.000000 1794.331000
75% 16.000000 0.094585 185.643700 2004.000000 3101.296400
max 21.350000 0.328391 266.888400 2009.000000 13086.964800

There is a massive difference between Item_Outlet_Sales 75% mark and the max, likely indicating a lot of outliers. Lets check this out using a boxplot.¶

In [16]:
fig, ax = plt.subplots()
ax.boxplot([df["Item_Outlet_Sales"]],labels = ["Item Sales"],
           notch=True,
           patch_artist=True)
plt.show()

Yea, this definitley looks odd, lets get a count of the number of outliers.¶

In [17]:
sales = df['Item_Outlet_Sales']
q1 = sales.quantile(0.25)
q3 = sales.quantile(0.75)
iqr = q3 - q1

outliers = df[sales > q3 + iqr * 1.5]
outliers
Out[17]:
Item_Identifier Item_Weight Item_Fat_Content Item_Visibility Item_Type Item_MRP Outlet_Identifier Outlet_Establishment_Year Outlet_Size Outlet_Location_Type Outlet_Type Item_Outlet_Sales
43 FDC02 21.350000 Low Fat 0.069103 Canned 259.9278 OUT018 2009 Medium Tier 3 Supermarket Type2 6768.5228
130 FDY25 12.305705 Low Fat 0.033810 Canned 180.5976 OUT027 1985 Medium Tier 3 Supermarket Type3 7968.2944
132 NCR53 13.142314 Low Fat 0.144338 Health and Hygiene 224.4404 OUT027 1985 Medium Tier 3 Supermarket Type3 6976.2524
145 FDP16 18.600000 Low Fat 0.039356 Frozen Foods 246.3802 OUT049 1999 Medium Tier 1 Supermarket Type1 7370.4060
203 FDI24 12.277108 Low Fat 0.078362 Baking Goods 177.9370 OUT027 1985 Medium Tier 3 Supermarket Type3 6704.6060
... ... ... ... ... ... ... ... ... ... ... ... ...
8245 FDU55 16.200000 Low Fat 0.035967 Fruits and Vegetables 260.3278 OUT049 1999 Medium Tier 1 Supermarket Type1 7549.5062
8329 NCQ06 13.384736 Low Fat 0.041622 Household 253.6014 OUT027 1985 Medium Tier 3 Supermarket Type3 6630.0364
8350 NCE18 10.000000 Low Fat 0.021421 Household 248.3750 OUT035 2004 Small Tier 2 Supermarket Type1 7240.5750
8447 FDS26 20.350000 Low Fat 0.089975 Dairy 261.6594 OUT017 2007 Unknown Tier 2 Supermarket Type1 7588.1226
8510 FDN58 13.800000 Regular 0.056862 Snack Foods 231.5984 OUT035 2004 Small Tier 2 Supermarket Type1 7182.6504

186 rows × 12 columns

186 outliers. Lets drop this data for now¶

In [18]:
df.drop(outliers.index,axis=0 ,inplace=True)
df.describe()
Out[18]:
Item_Weight Item_Visibility Item_MRP Outlet_Establishment_Year Item_Outlet_Sales
count 8337.000000 8337.000000 8337.000000 8337.000000 8337.000000
mean 12.856296 0.066358 139.161087 1997.968094 2059.362844
std 4.256788 0.051838 61.553351 8.309941 1506.099754
min 4.555000 0.000000 31.290000 1985.000000 33.290000
25% 9.300000 0.027028 93.046200 1987.000000 810.944400
50% 12.867061 0.053939 141.215400 1999.000000 1747.059200
75% 16.100000 0.095299 183.695000 2004.000000 2998.097400
max 21.350000 0.328391 266.888400 2009.000000 6478.234000

Exploratory Plots¶

There are many factors which can contribute to the sales of an object, so lets start by looking into any corellation between numerical values¶

In [19]:
sns.heatmap(df.corr(),cmap="Blues",annot= True)
Out[19]:
<matplotlib.axes._subplots.AxesSubplot at 0x7f75c9874a50>

This heatmap tells us there is a correlation between the Maximum Retail Price of an Item and the total revenue produced from sale of the item at an outlet, which makes logical sense, as a more expensive product provides more revenue per item sold.¶

More importantly it tells us that item visibilty has a negative correlation with total revenue generated, meaning that this may be a way to predict future sales. We will explore this more with a scatter plot¶

In [20]:
x = df['Item_Visibility']
y = df['Item_Outlet_Sales']

a,b = np.polyfit(x,y,1)

fig, ax = plt.subplots()

ax.scatter(x,y, label = "datapoint")
ax.plot(x,a*x+b, "r-", label="Best Fit Line")
ax.legend()
ax.set_title("Total Sales Revenue by Visibility")
ax.set_xlabel("Visiblity")
ax.set_ylabel("Sales Revenue")
plt.show()

From the previous scatter plot we can see that there is a negative correlation between visibility and sales, with a large drop off at approximately 0.19 visibility¶

Now lets look at some catgorical data. We will start with Outlet Size¶

In [21]:
histogram = df.groupby("Outlet_Size")["Item_Outlet_Sales"].hist(alpha=0.35,legend=True)

While most of the different sizes seem to fall into similar patterns, the Medium sized outlets seem to have a larger percentage of high sales items in comparison to the other sizes, We will visualize this in a different way using a boxplot¶

In [22]:
large = df[df["Outlet_Size"]=="Large"]["Item_Outlet_Sales"]
med = df[df["Outlet_Size"]=="Medium"]["Item_Outlet_Sales"]
small = df[df["Outlet_Size"]=="Small"]["Item_Outlet_Sales"]
uk = df[df["Outlet_Size"]=="Unknown"]["Item_Outlet_Sales"]


fig, ax = plt.subplots()
bps = ax.boxplot([small, med, large, uk],labels=["Small","Medium","Large","Unknown"], notch=True,
                 patch_artist=True)
plt.show()

/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning:

Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
In [23]:
df.groupby("Outlet_Size")["Item_Outlet_Sales"].describe()
Out[23]:
count mean std min 25% 50% 75% max
Outlet_Size
Large 917.0 2217.100205 1398.677744 73.2380 1057.95620 2014.7108 3103.95960 6474.2392
Medium 2676.0 2458.690424 1530.464302 69.2432 1238.22155 2135.8864 3448.84400 6478.2340
Small 2362.0 1852.606258 1483.026265 33.9558 593.22780 1517.3582 2775.55375 6474.2392
Unknown 2382.0 1755.044328 1432.707736 33.2900 549.28500 1417.4882 2631.24160 6471.5760

Here we are confirming our assessment that Medium sized outlets have higher average sales per item, based on the higher mean, median, and range¶

Explanatory diagrams¶

In order for the Client to have a better idea of the data we are working with we will organize the Total sales based on a few criteria¶

First we will explore Sales Per Outlet, grouped by size¶

In [24]:
fig = px.histogram(df,x="Outlet_Identifier", y="Item_Outlet_Sales",
                   title="Total Sales Per Outlet",
                   hover_data=['Outlet_Size'],
                   color ="Outlet_Size",
                   labels = {"Outlet_Size": "Outlet Size",
                             "Outlet_Identifier": "Outlet ID",
                             "Item_Outlet_Sales": "Total Sales"},
                   category_orders = {"Outlet_Size":["Small",
                                                     "Medium",
                                                     "Large",
                                                     "Unknown"]})
py.iplot(fig)

Next we will explore Sales by Item Type, grouped by Outlet Type¶

In [25]:
fig = px.histogram(df,x="Item_Type", y="Item_Outlet_Sales",
                   title="Total Sales Per Item Type",
                   hover_data=['Outlet_Type'],
                   color ="Outlet_Type",
                   labels = {"Outlet_Type": "Outlet Type",
                             "Item_Type": "Item Type",
                             "Item_Outlet_Sales": "Total Sales"},
                   category_orders = {"Outlet_Type":["Supermarket Type1",
                                                     "Supermarket Type2",
                                                     "Supermarket Type3",
                                                     "Grocery Store"]},)

py.iplot(fig)

With these interactive Graphs the Client can easily look for trends in the data and use them to make business decisions.¶

Machine Learning¶

With the power of Machine learning, meaningful data analysis can be done automatically, and predictions can be made relatively accurately by the computer¶

Lets start by importing the modules we will need¶

In [26]:
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OrdinalEncoder

Now we will seperate the data into our prediction's target (Item_Outlet_Sales) and the data used to estimate that target. We will also seperate out test data from validation data to prevent data leaks.¶

In [27]:
y = df["Item_Outlet_Sales"]

numeric_features = ["Item_Weight",
                    "Item_Visibility",
                    "Item_MRP",
                    "Outlet_Establishment_Year"]
ordinal_encoded_features = ["Outlet_Size",
                            "Outlet_Type",
                            "Outlet_Identifier",
                            "Item_Type",
                            "Item_Fat_Content"]

# Include all fatures to be included in the model
X = df[numeric_features + ordinal_encoded_features]
X.head()
Out[27]:
Item_Weight Item_Visibility Item_MRP Outlet_Establishment_Year Outlet_Size Outlet_Type Outlet_Identifier Item_Type Item_Fat_Content
0 9.30 0.016047 249.8092 1999 Medium Supermarket Type1 OUT049 Dairy Low Fat
1 5.92 0.019278 48.2692 2009 Medium Supermarket Type2 OUT018 Soft Drinks Regular
2 17.50 0.016760 141.6180 1999 Medium Supermarket Type1 OUT049 Meat Low Fat
3 19.20 0.000000 182.0950 1998 Unknown Grocery Store OUT010 Fruits and Vegetables Regular
4 8.93 0.000000 53.8614 1987 Large Supermarket Type1 OUT013 Household Low Fat

Lets split the data now to avoid any data leaks¶

In [28]:
X_train, X_valid, y_train, y_valid = train_test_split(X, y, random_state=1)

Now lets convert the objects into numbers for the model to use¶

In [29]:
ord_enc = OrdinalEncoder()
X_train[ordinal_encoded_features] = ord_enc.fit_transform(X_train[ordinal_encoded_features],y_train)
X_valid[ordinal_encoded_features] = ord_enc.transform(X_valid[ordinal_encoded_features])
X_train.head()
Out[29]:
Item_Weight Item_Visibility Item_MRP Outlet_Establishment_Year Outlet_Size Outlet_Type Outlet_Identifier Item_Type Item_Fat_Content
6613 8.630000 0.032961 115.0518 1999 1.0 1.0 9.0 13.0 1.0
3895 20.100000 0.000000 61.3536 1998 3.0 0.0 0.0 8.0 0.0
5894 12.817344 0.000000 98.2042 1985 2.0 0.0 4.0 10.0 1.0
5726 21.100000 0.000000 233.2958 2009 1.0 2.0 3.0 9.0 0.0
1771 13.500000 0.021496 180.5976 1997 2.0 1.0 8.0 6.0 0.0

Lets make and train the model now¶

In [30]:
model = RandomForestRegressor(random_state=1) # random state is so that testing can be measured reliably

model.fit(X_train,y_train)
Out[30]:
RandomForestRegressor(random_state=1)

We need a way to test the effectiveness of the model¶

In [31]:
def get_MAE(model,X,y):
  return mean_absolute_error(model.predict(X),y)

def get_percent(model,X,y):
  return 1-(get_MAE(model,X,y)/y.mean())
def print_stats(model,X,y):
  print("The MAE for this model is {:.2f}".format(get_MAE(model,X,y)))
  print("The accuracy for this model is {:%}".format(get_percent(model,X,y)))

print_stats(model,X_valid,y_valid)

The MAE for this model is 744.32
The accuracy for this model is 63.953409%

Well thats slightly better than flipping a coin... Lets try to improve this model a bit. We can start by defining the number of estimators.¶

In [32]:
results = {}
for i in range(1,7):
  test_model = RandomForestRegressor(n_estimators=i*50,random_state=1)
  test_model.fit(X_train,y_train)
  results[i*50] = get_MAE(test_model,X_valid,y_valid)
  print(f"{i*50}:")
  print_stats(test_model,X_valid,y_valid)

50:
The MAE for this model is 743.92
The accuracy for this model is 63.972851%
100:
The MAE for this model is 744.32
The accuracy for this model is 63.953409%
150:
The MAE for this model is 743.67
The accuracy for this model is 63.984932%
200:
The MAE for this model is 742.69
The accuracy for this model is 64.032610%
250:
The MAE for this model is 741.80
The accuracy for this model is 64.075361%
300:
The MAE for this model is 741.77
The accuracy for this model is 64.077056%
In [33]:
plt.plot([*results.keys()],[*results.values()] )
plt.gca().invert_yaxis()
plt.show()

This isn't helping by much (less than 1% difference), so we should consider using a different algorithm, Lets try a Gradient Boosting Algorithm.¶

In [34]:
from xgboost.sklearn import XGBRegressor
In [35]:
model2 = XGBRegressor(n_estimators = 2000,
                     learning_rate=0.005,
                     early_stopping_rounds = 10,
                     random_state= 1)

model2.fit(X_train,y_train,
          eval_set= [(X_valid, y_valid)],
          verbose = False)

print_stats(model2,X_valid,y_valid)

[16:24:24] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
The MAE for this model is 706.50
The accuracy for this model is 65.784921%

There is some improvement, however still not enough to accurately predict sales, so lets create a deep nueral network.¶

In [36]:
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers, callbacks
In [37]:
model3 = keras.Sequential([
    layers.BatchNormalization(input_shape = [X_train.shape[1]]),
    layers.Dense(256,activation="relu"),
    layers.Dropout(0.3),
    layers.BatchNormalization(),
    layers.Dense(256,activation="relu"),
    layers.Dropout(0.3),
    layers.Dense(1)
])
early_stopping = callbacks.EarlyStopping(
    min_delta = 0.001,
    patience = 20,
    restore_best_weights = True
)
In [38]:
model3.compile(
    optimizer="adam",
    loss="mae",
    metrics=['mae'],
)
history = model3.fit(
    X_train, y_train,
    validation_data=(X_valid, y_valid),
    batch_size=128,
    callbacks=[early_stopping],
    epochs=100,
    verbose=0,
)
history_df = pd.DataFrame(history.history)
history_df['val_loss'].plot()
best_results = history_df['val_loss'].min()
print("Minimum Validation Loss: {:0.4f}".format(best_results))
print("Highest Accuracy: {:0.4%}".format(1-(best_results/df['Item_Outlet_Sales'].mean())))

Minimum Validation Loss: 700.5079
Highest Accuracy: 65.9842%

Again we are improving, but not by enough to be useful. We need to improve our data. Lets try using GridSearchCV!¶

In [39]:
from sklearn.model_selection import GridSearchCV

Lets start by using grid search on our RandomForestRegressor¶

In [40]:
param_grid = {
    "n_estimators": [x for x in range(100,251,50)],
    "max_features": [1.0,"sqrt","log2"],
    "min_weight_fraction_leaf": [0.0, 0.1, 0.01],
}


grid_search = GridSearchCV( estimator = RandomForestRegressor(),
                            param_grid = param_grid,
                            scoring = "neg_mean_absolute_error",
                            n_jobs = 4,
                            verbose = 2)
In [41]:
grid_search.fit(X_train, y_train)

Fitting 5 folds for each of 36 candidates, totalling 180 fits
Out[41]:
GridSearchCV(estimator=RandomForestRegressor(), n_jobs=4,
             param_grid={'max_features': [1.0, 'sqrt', 'log2'],
                         'min_weight_fraction_leaf': [0.0, 0.1, 0.01],
                         'n_estimators': [100, 150, 200, 250]},
             scoring='neg_mean_absolute_error', verbose=2)
In [42]:
print_stats(grid_search,X_valid,y_valid)

The MAE for this model is 702.43
The accuracy for this model is 65.982081%
In [43]:
grid_search.best_params_
Out[43]:
{'max_features': 1.0, 'min_weight_fraction_leaf': 0.01, 'n_estimators': 100}
In [44]:
from joblib import dump
dump(grid_search,"model.joblib")
Out[44]:
['model.joblib']